Robust Guidewire Tracking in Fluoroscopy

Abstract

A guidewire is a medical device inserted into vessels during image guided interventions for balloon inflation. During interventions, the guidewire undergoes non-rigid deformation due to patients' breathing and cardiac motions, and such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, in fluoroscopy there exist severe image artifacts and other wire-like structures. All these make robust guidewire tracking challenging. To address these challenges, this paper presents a probabilistic framework for robust guidewire tracking. We first introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. Moreover, for each part, two types of measurements, one from learning-based detectors and the other from online appearance models, are applied and combined. A hierarchical and multi-resolution tracking scheme is then developed based on kernel-based measurement smoothing to track guidewires effectively and efficiently in a coarse-to-fine manner. The presented framework has been validated on a test set of 47 sequences, and achieves a mean tracking error of less than 2 pixels. This demonstrates the great potential of our method for clinical applications.